AGMNet: an explainable gated multimodal framework integrating MRI heterogeneity and dopaminergic asymmetry for Parkinson's disease classification

Introduction Parkinson's Disease (PD) is a neurological disorder that worsens over time and is marked by interhemispheric asymmetry, dopaminergic degeneration, and structural changes in the brain. Methods This paper presents a novel approach: AGMNet- Adaptive Gated Multimodel Network, which is an explainable gated multimodal deep learning framework that combines structural magnetic resonance imaging (MRI), dopamine transporter (DaT) imaging, asymmetry-aware representations, and biomarker features for robust PD classification. The Grad-CAM method is incorporated with the proposed AGMNet framework to improve the model interpretability by identifying image regions that influence classification decisions. The performance of AGMNet framework was evaluated using five-fold cross-validation and compared with established deep learning architectures, including VGG16, VGG19, InceptionV3, and Xception. Results The proposed AGMNet has achieved 0.8318 of accuracy, 0.8278 of balanced accuracy, sensitivity of 0.8556, specificity of 0.8000, AUC of 0.711, and Matthews correlation coefficient (MCC) of 0.6458. The proposed AGMNet model demonstrated competitive classification performance relative to the evaluated baseline architectures. The combination of DaT imaging, asymmetry-aware features, and biomarker representations in the proposed model, contributed more strongly to the fused representation than MRI features alone. Discussion The findings indicate that adaptive multimodal fusion in the AGMNet framework can improve the integration of complementary structural, functional, asymmetry-related, and biomarker information for PD classification. The learned gate weights further demonstrate the relevance of functional and asymmetry-aware information within the proposed framework, while Grad-CAM provides additional insight into model decision-making. Therefore, the AGMNet framework offers a potentially useful and interpretable framework for multimodal PD classification.